The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following drawbacks: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility in representations; ii) Due to the absence of non-observed samples across multiple views, the obtained prototypes of clusters might be unaligned and biased, leading to incorrect fusion. To address the above issues, we propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction among views. Then, regarding of the shifted prototypes in IMVC, we further propose a prototype alignment module to achieve incomplete distribution calibration across views. Extensive experimental results showcase the effectiveness of our proposed modules, attaining noteworthy performance improvements when compared to existing IMVC competitors on benchmark datasets.

翻译：现有多视图聚类的成功依赖于各视图下样本完整性的假设。然而，在现实场景中，由于数据损坏或传感器故障，多视图样本可能部分缺失，从而催生了不完整多视图聚类（IMVC）研究。尽管已有多种方法尝试解决IMVC问题，但仍存在以下缺陷：i) 现有方法主要采用跨视图对比学习，强制要求各视图下每个样本的表征完全一致，这可能忽略了视图差异性与表征的灵活性；ii) 由于多视图间存在缺失样本，所获得的聚类原型可能未对齐且存在偏差，导致融合错误。针对上述问题，我们提出了一种用于深度不完整多视图聚类的跨视图部分样本与原型对齐网络（CPSPAN）。首先，与现有基于对比的方法不同，我们采用成对观测数据对齐作为"代理监督信号"，引导视图间实例到实例的对应关系构建。其次，针对IMVC中偏移的原型，我们进一步提出原型对齐模块，以实现跨视图的不完整分布校准。大量实验结果表明，我们提出的模块具有有效性，在基准数据集上与现有IMVC方法相比取得了显著的性能提升。